VISAS -- Detecting GPS spoofing attacks against drones by analyzing camera's video stream
This addresses security vulnerabilities for drone operators by providing a real-time detection method, though it appears incremental as it builds on existing video analysis techniques for spoofing detection.
The study tackled GPS spoofing attacks on drones by proposing a method that analyzes video stream frames and their GPS locations to detect attacks in real-time, achieving detection of spoofed locations within an average distance of 2.5 meters from the real location in specific scenarios.
In this study, we propose an innovative method for the real-time detection of GPS spoofing attacks targeting drones, based on the video stream captured by a drone's camera. The proposed method collects frames from the video stream and their location (GPS); by calculating the correlation between each frame, our method can identify an attack on a drone. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at an altitude of 50-100 meters over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 meters (an average of 2.5 meters) from the real location.